Analysis of 111/999 NHS calls and 111 online reports
Quentin Leclerc, Emily Nightingale, Samuel Abbott & Thibaut Jombart, for CMMID
Tuesday 16 June 2020
1 Data preparation
1.1 Outline
Load scripts: loads libraries and useful scripts used in the analyses; all
.Rfiles contained inscriptsat the root of the factory are automatically loadedLoad data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
1.2 Load packages
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)1.3 Load scripts
These scripts will load:
- all scripts stored as
.Rfiles inside/scripts/ - all scripts stored as
.Rfiles inside/src/
These scripts also contain routines to access the latest clean encrypted data (see next section).
1.4 Load clean data
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 147,168 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 35 rm13ae London
## [90m 2[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bed… 27 mk454hr East of E…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bla… 9 bb12fd North West
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bro… 11 br33ql London
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_can… 9 ws111jp Midlands
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_cit… 12 n15lz London
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_enf… 7 en40dy London
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ham… 6 dl62uu North Eas…
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_har… 24 ts232la North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_kin… 6 kt11eu London
## [90m# … with 147,158 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 62
## 33 2020-04-02 East of England 64
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 91
## 43 2020-04-12 East of England 101
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 36
## 67 2020-05-06 East of England 30
## 68 2020-05-07 East of England 33
## 69 2020-05-08 East of England 33
## 70 2020-05-09 East of England 29
## 71 2020-05-10 East of England 22
## 72 2020-05-11 East of England 18
## 73 2020-05-12 East of England 21
## 74 2020-05-13 East of England 27
## 75 2020-05-14 East of England 26
## 76 2020-05-15 East of England 19
## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
## 79 2020-05-18 East of England 25
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
## 83 2020-05-22 East of England 13
## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 16
## 86 2020-05-25 East of England 25
## 87 2020-05-26 East of England 14
## 88 2020-05-27 East of England 12
## 89 2020-05-28 East of England 17
## 90 2020-05-29 East of England 15
## 91 2020-05-30 East of England 9
## 92 2020-05-31 East of England 8
## 93 2020-06-01 East of England 17
## 94 2020-06-02 East of England 14
## 95 2020-06-03 East of England 10
## 96 2020-06-04 East of England 7
## 97 2020-06-05 East of England 12
## 98 2020-06-06 East of England 4
## 99 2020-06-07 East of England 9
## 100 2020-06-08 East of England 5
## 101 2020-06-09 East of England 4
## 102 2020-06-10 East of England 7
## 103 2020-06-11 East of England 0
## 104 2020-06-12 East of England 5
## 105 2020-06-13 East of England 2
## 106 2020-06-14 East of England 0
## 107 2020-03-01 London 0
## 108 2020-03-02 London 0
## 109 2020-03-03 London 0
## 110 2020-03-04 London 0
## 111 2020-03-05 London 0
## 112 2020-03-06 London 1
## 113 2020-03-07 London 1
## 114 2020-03-08 London 0
## 115 2020-03-09 London 1
## 116 2020-03-10 London 0
## 117 2020-03-11 London 7
## 118 2020-03-12 London 6
## 119 2020-03-13 London 10
## 120 2020-03-14 London 14
## 121 2020-03-15 London 10
## 122 2020-03-16 London 18
## 123 2020-03-17 London 25
## 124 2020-03-18 London 31
## 125 2020-03-19 London 25
## 126 2020-03-20 London 44
## 127 2020-03-21 London 50
## 128 2020-03-22 London 54
## 129 2020-03-23 London 64
## 130 2020-03-24 London 87
## 131 2020-03-25 London 113
## 132 2020-03-26 London 130
## 133 2020-03-27 London 130
## 134 2020-03-28 London 122
## 135 2020-03-29 London 147
## 136 2020-03-30 London 150
## 137 2020-03-31 London 181
## 138 2020-04-01 London 202
## 139 2020-04-02 London 190
## 140 2020-04-03 London 196
## 141 2020-04-04 London 230
## 142 2020-04-05 London 195
## 143 2020-04-06 London 198
## 144 2020-04-07 London 219
## 145 2020-04-08 London 238
## 146 2020-04-09 London 206
## 147 2020-04-10 London 170
## 148 2020-04-11 London 177
## 149 2020-04-12 London 158
## 150 2020-04-13 London 166
## 151 2020-04-14 London 144
## 152 2020-04-15 London 142
## 153 2020-04-16 London 139
## 154 2020-04-17 London 100
## 155 2020-04-18 London 101
## 156 2020-04-19 London 103
## 157 2020-04-20 London 95
## 158 2020-04-21 London 95
## 159 2020-04-22 London 109
## 160 2020-04-23 London 77
## 161 2020-04-24 London 71
## 162 2020-04-25 London 58
## 163 2020-04-26 London 53
## 164 2020-04-27 London 51
## 165 2020-04-28 London 43
## 166 2020-04-29 London 44
## 167 2020-04-30 London 40
## 168 2020-05-01 London 41
## 169 2020-05-02 London 40
## 170 2020-05-03 London 36
## 171 2020-05-04 London 30
## 172 2020-05-05 London 25
## 173 2020-05-06 London 37
## 174 2020-05-07 London 37
## 175 2020-05-08 London 29
## 176 2020-05-09 London 23
## 177 2020-05-10 London 26
## 178 2020-05-11 London 18
## 179 2020-05-12 London 18
## 180 2020-05-13 London 16
## 181 2020-05-14 London 20
## 182 2020-05-15 London 18
## 183 2020-05-16 London 14
## 184 2020-05-17 London 15
## 185 2020-05-18 London 9
## 186 2020-05-19 London 13
## 187 2020-05-20 London 19
## 188 2020-05-21 London 12
## 189 2020-05-22 London 10
## 190 2020-05-23 London 6
## 191 2020-05-24 London 7
## 192 2020-05-25 London 9
## 193 2020-05-26 London 12
## 194 2020-05-27 London 7
## 195 2020-05-28 London 8
## 196 2020-05-29 London 7
## 197 2020-05-30 London 12
## 198 2020-05-31 London 6
## 199 2020-06-01 London 10
## 200 2020-06-02 London 7
## 201 2020-06-03 London 6
## 202 2020-06-04 London 8
## 203 2020-06-05 London 3
## 204 2020-06-06 London 0
## 205 2020-06-07 London 4
## 206 2020-06-08 London 5
## 207 2020-06-09 London 2
## 208 2020-06-10 London 7
## 209 2020-06-11 London 5
## 210 2020-06-12 London 2
## 211 2020-06-13 London 2
## 212 2020-06-14 London 0
## 213 2020-03-01 Midlands 0
## 214 2020-03-02 Midlands 0
## 215 2020-03-03 Midlands 1
## 216 2020-03-04 Midlands 0
## 217 2020-03-05 Midlands 0
## 218 2020-03-06 Midlands 0
## 219 2020-03-07 Midlands 0
## 220 2020-03-08 Midlands 3
## 221 2020-03-09 Midlands 1
## 222 2020-03-10 Midlands 0
## 223 2020-03-11 Midlands 2
## 224 2020-03-12 Midlands 6
## 225 2020-03-13 Midlands 5
## 226 2020-03-14 Midlands 4
## 227 2020-03-15 Midlands 5
## 228 2020-03-16 Midlands 11
## 229 2020-03-17 Midlands 8
## 230 2020-03-18 Midlands 13
## 231 2020-03-19 Midlands 8
## 232 2020-03-20 Midlands 28
## 233 2020-03-21 Midlands 13
## 234 2020-03-22 Midlands 31
## 235 2020-03-23 Midlands 33
## 236 2020-03-24 Midlands 41
## 237 2020-03-25 Midlands 48
## 238 2020-03-26 Midlands 64
## 239 2020-03-27 Midlands 72
## 240 2020-03-28 Midlands 89
## 241 2020-03-29 Midlands 92
## 242 2020-03-30 Midlands 90
## 243 2020-03-31 Midlands 123
## 244 2020-04-01 Midlands 140
## 245 2020-04-02 Midlands 142
## 246 2020-04-03 Midlands 124
## 247 2020-04-04 Midlands 151
## 248 2020-04-05 Midlands 164
## 249 2020-04-06 Midlands 140
## 250 2020-04-07 Midlands 123
## 251 2020-04-08 Midlands 186
## 252 2020-04-09 Midlands 139
## 253 2020-04-10 Midlands 127
## 254 2020-04-11 Midlands 142
## 255 2020-04-12 Midlands 139
## 256 2020-04-13 Midlands 120
## 257 2020-04-14 Midlands 116
## 258 2020-04-15 Midlands 147
## 259 2020-04-16 Midlands 102
## 260 2020-04-17 Midlands 118
## 261 2020-04-18 Midlands 115
## 262 2020-04-19 Midlands 92
## 263 2020-04-20 Midlands 107
## 264 2020-04-21 Midlands 86
## 265 2020-04-22 Midlands 78
## 266 2020-04-23 Midlands 103
## 267 2020-04-24 Midlands 79
## 268 2020-04-25 Midlands 72
## 269 2020-04-26 Midlands 81
## 270 2020-04-27 Midlands 74
## 271 2020-04-28 Midlands 68
## 272 2020-04-29 Midlands 53
## 273 2020-04-30 Midlands 56
## 274 2020-05-01 Midlands 64
## 275 2020-05-02 Midlands 51
## 276 2020-05-03 Midlands 52
## 277 2020-05-04 Midlands 61
## 278 2020-05-05 Midlands 58
## 279 2020-05-06 Midlands 59
## 280 2020-05-07 Midlands 48
## 281 2020-05-08 Midlands 34
## 282 2020-05-09 Midlands 37
## 283 2020-05-10 Midlands 42
## 284 2020-05-11 Midlands 33
## 285 2020-05-12 Midlands 45
## 286 2020-05-13 Midlands 39
## 287 2020-05-14 Midlands 37
## 288 2020-05-15 Midlands 40
## 289 2020-05-16 Midlands 34
## 290 2020-05-17 Midlands 31
## 291 2020-05-18 Midlands 34
## 292 2020-05-19 Midlands 34
## 293 2020-05-20 Midlands 36
## 294 2020-05-21 Midlands 32
## 295 2020-05-22 Midlands 27
## 296 2020-05-23 Midlands 34
## 297 2020-05-24 Midlands 19
## 298 2020-05-25 Midlands 26
## 299 2020-05-26 Midlands 33
## 300 2020-05-27 Midlands 29
## 301 2020-05-28 Midlands 27
## 302 2020-05-29 Midlands 20
## 303 2020-05-30 Midlands 20
## 304 2020-05-31 Midlands 21
## 305 2020-06-01 Midlands 20
## 306 2020-06-02 Midlands 21
## 307 2020-06-03 Midlands 23
## 308 2020-06-04 Midlands 15
## 309 2020-06-05 Midlands 21
## 310 2020-06-06 Midlands 20
## 311 2020-06-07 Midlands 16
## 312 2020-06-08 Midlands 15
## 313 2020-06-09 Midlands 17
## 314 2020-06-10 Midlands 14
## 315 2020-06-11 Midlands 13
## 316 2020-06-12 Midlands 8
## 317 2020-06-13 Midlands 1
## 318 2020-06-14 Midlands 2
## 319 2020-03-01 North East and Yorkshire 0
## 320 2020-03-02 North East and Yorkshire 0
## 321 2020-03-03 North East and Yorkshire 0
## 322 2020-03-04 North East and Yorkshire 0
## 323 2020-03-05 North East and Yorkshire 0
## 324 2020-03-06 North East and Yorkshire 0
## 325 2020-03-07 North East and Yorkshire 0
## 326 2020-03-08 North East and Yorkshire 0
## 327 2020-03-09 North East and Yorkshire 0
## 328 2020-03-10 North East and Yorkshire 0
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## 330 2020-03-12 North East and Yorkshire 0
## 331 2020-03-13 North East and Yorkshire 0
## 332 2020-03-14 North East and Yorkshire 0
## 333 2020-03-15 North East and Yorkshire 2
## 334 2020-03-16 North East and Yorkshire 3
## 335 2020-03-17 North East and Yorkshire 1
## 336 2020-03-18 North East and Yorkshire 2
## 337 2020-03-19 North East and Yorkshire 6
## 338 2020-03-20 North East and Yorkshire 5
## 339 2020-03-21 North East and Yorkshire 6
## 340 2020-03-22 North East and Yorkshire 7
## 341 2020-03-23 North East and Yorkshire 9
## 342 2020-03-24 North East and Yorkshire 8
## 343 2020-03-25 North East and Yorkshire 18
## 344 2020-03-26 North East and Yorkshire 21
## 345 2020-03-27 North East and Yorkshire 28
## 346 2020-03-28 North East and Yorkshire 35
## 347 2020-03-29 North East and Yorkshire 38
## 348 2020-03-30 North East and Yorkshire 64
## 349 2020-03-31 North East and Yorkshire 60
## 350 2020-04-01 North East and Yorkshire 67
## 351 2020-04-02 North East and Yorkshire 74
## 352 2020-04-03 North East and Yorkshire 100
## 353 2020-04-04 North East and Yorkshire 105
## 354 2020-04-05 North East and Yorkshire 92
## 355 2020-04-06 North East and Yorkshire 96
## 356 2020-04-07 North East and Yorkshire 102
## 357 2020-04-08 North East and Yorkshire 107
## 358 2020-04-09 North East and Yorkshire 111
## 359 2020-04-10 North East and Yorkshire 117
## 360 2020-04-11 North East and Yorkshire 98
## 361 2020-04-12 North East and Yorkshire 84
## 362 2020-04-13 North East and Yorkshire 94
## 363 2020-04-14 North East and Yorkshire 107
## 364 2020-04-15 North East and Yorkshire 96
## 365 2020-04-16 North East and Yorkshire 103
## 366 2020-04-17 North East and Yorkshire 88
## 367 2020-04-18 North East and Yorkshire 95
## 368 2020-04-19 North East and Yorkshire 88
## 369 2020-04-20 North East and Yorkshire 100
## 370 2020-04-21 North East and Yorkshire 76
## 371 2020-04-22 North East and Yorkshire 84
## 372 2020-04-23 North East and Yorkshire 63
## 373 2020-04-24 North East and Yorkshire 72
## 374 2020-04-25 North East and Yorkshire 69
## 375 2020-04-26 North East and Yorkshire 65
## 376 2020-04-27 North East and Yorkshire 65
## 377 2020-04-28 North East and Yorkshire 57
## 378 2020-04-29 North East and Yorkshire 69
## 379 2020-04-30 North East and Yorkshire 57
## 380 2020-05-01 North East and Yorkshire 64
## 381 2020-05-02 North East and Yorkshire 48
## 382 2020-05-03 North East and Yorkshire 40
## 383 2020-05-04 North East and Yorkshire 49
## 384 2020-05-05 North East and Yorkshire 40
## 385 2020-05-06 North East and Yorkshire 50
## 386 2020-05-07 North East and Yorkshire 45
## 387 2020-05-08 North East and Yorkshire 42
## 388 2020-05-09 North East and Yorkshire 44
## 389 2020-05-10 North East and Yorkshire 40
## 390 2020-05-11 North East and Yorkshire 29
## 391 2020-05-12 North East and Yorkshire 27
## 392 2020-05-13 North East and Yorkshire 28
## 393 2020-05-14 North East and Yorkshire 30
## 394 2020-05-15 North East and Yorkshire 32
## 395 2020-05-16 North East and Yorkshire 35
## 396 2020-05-17 North East and Yorkshire 26
## 397 2020-05-18 North East and Yorkshire 29
## 398 2020-05-19 North East and Yorkshire 27
## 399 2020-05-20 North East and Yorkshire 21
## 400 2020-05-21 North East and Yorkshire 33
## 401 2020-05-22 North East and Yorkshire 22
## 402 2020-05-23 North East and Yorkshire 18
## 403 2020-05-24 North East and Yorkshire 25
## 404 2020-05-25 North East and Yorkshire 21
## 405 2020-05-26 North East and Yorkshire 21
## 406 2020-05-27 North East and Yorkshire 21
## 407 2020-05-28 North East and Yorkshire 20
## 408 2020-05-29 North East and Yorkshire 24
## 409 2020-05-30 North East and Yorkshire 20
## 410 2020-05-31 North East and Yorkshire 19
## 411 2020-06-01 North East and Yorkshire 16
## 412 2020-06-02 North East and Yorkshire 22
## 413 2020-06-03 North East and Yorkshire 22
## 414 2020-06-04 North East and Yorkshire 17
## 415 2020-06-05 North East and Yorkshire 17
## 416 2020-06-06 North East and Yorkshire 20
## 417 2020-06-07 North East and Yorkshire 13
## 418 2020-06-08 North East and Yorkshire 11
## 419 2020-06-09 North East and Yorkshire 11
## 420 2020-06-10 North East and Yorkshire 15
## 421 2020-06-11 North East and Yorkshire 4
## 422 2020-06-12 North East and Yorkshire 8
## 423 2020-06-13 North East and Yorkshire 5
## 424 2020-06-14 North East and Yorkshire 1
## 425 2020-03-01 North West 0
## 426 2020-03-02 North West 0
## 427 2020-03-03 North West 0
## 428 2020-03-04 North West 0
## 429 2020-03-05 North West 1
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## 431 2020-03-07 North West 0
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## 433 2020-03-09 North West 0
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## 438 2020-03-14 North West 1
## 439 2020-03-15 North West 4
## 440 2020-03-16 North West 2
## 441 2020-03-17 North West 4
## 442 2020-03-18 North West 6
## 443 2020-03-19 North West 7
## 444 2020-03-20 North West 10
## 445 2020-03-21 North West 11
## 446 2020-03-22 North West 13
## 447 2020-03-23 North West 16
## 448 2020-03-24 North West 21
## 449 2020-03-25 North West 21
## 450 2020-03-26 North West 29
## 451 2020-03-27 North West 35
## 452 2020-03-28 North West 28
## 453 2020-03-29 North West 46
## 454 2020-03-30 North West 67
## 455 2020-03-31 North West 52
## 456 2020-04-01 North West 86
## 457 2020-04-02 North West 96
## 458 2020-04-03 North West 95
## 459 2020-04-04 North West 98
## 460 2020-04-05 North West 102
## 461 2020-04-06 North West 100
## 462 2020-04-07 North West 134
## 463 2020-04-08 North West 127
## 464 2020-04-09 North West 119
## 465 2020-04-10 North West 117
## 466 2020-04-11 North West 139
## 467 2020-04-12 North West 126
## 468 2020-04-13 North West 129
## 469 2020-04-14 North West 131
## 470 2020-04-15 North West 114
## 471 2020-04-16 North West 134
## 472 2020-04-17 North West 98
## 473 2020-04-18 North West 113
## 474 2020-04-19 North West 71
## 475 2020-04-20 North West 83
## 476 2020-04-21 North West 76
## 477 2020-04-22 North West 86
## 478 2020-04-23 North West 85
## 479 2020-04-24 North West 66
## 480 2020-04-25 North West 65
## 481 2020-04-26 North West 55
## 482 2020-04-27 North West 54
## 483 2020-04-28 North West 57
## 484 2020-04-29 North West 62
## 485 2020-04-30 North West 59
## 486 2020-05-01 North West 45
## 487 2020-05-02 North West 56
## 488 2020-05-03 North West 55
## 489 2020-05-04 North West 48
## 490 2020-05-05 North West 48
## 491 2020-05-06 North West 44
## 492 2020-05-07 North West 49
## 493 2020-05-08 North West 42
## 494 2020-05-09 North West 30
## 495 2020-05-10 North West 41
## 496 2020-05-11 North West 34
## 497 2020-05-12 North West 38
## 498 2020-05-13 North West 25
## 499 2020-05-14 North West 26
## 500 2020-05-15 North West 33
## 501 2020-05-16 North West 32
## 502 2020-05-17 North West 24
## 503 2020-05-18 North West 31
## 504 2020-05-19 North West 35
## 505 2020-05-20 North West 27
## 506 2020-05-21 North West 26
## 507 2020-05-22 North West 26
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## 509 2020-05-24 North West 26
## 510 2020-05-25 North West 31
## 511 2020-05-26 North West 27
## 512 2020-05-27 North West 27
## 513 2020-05-28 North West 28
## 514 2020-05-29 North West 20
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## 516 2020-05-31 North West 13
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## 518 2020-06-02 North West 27
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## 522 2020-06-06 North West 23
## 523 2020-06-07 North West 17
## 524 2020-06-08 North West 19
## 525 2020-06-09 North West 15
## 526 2020-06-10 North West 12
## 527 2020-06-11 North West 14
## 528 2020-06-12 North West 4
## 529 2020-06-13 North West 3
## 530 2020-06-14 North West 2
## 531 2020-03-01 South East 0
## 532 2020-03-02 South East 0
## 533 2020-03-03 South East 1
## 534 2020-03-04 South East 0
## 535 2020-03-05 South East 1
## 536 2020-03-06 South East 0
## 537 2020-03-07 South East 0
## 538 2020-03-08 South East 1
## 539 2020-03-09 South East 1
## 540 2020-03-10 South East 1
## 541 2020-03-11 South East 1
## 542 2020-03-12 South East 0
## 543 2020-03-13 South East 1
## 544 2020-03-14 South East 1
## 545 2020-03-15 South East 5
## 546 2020-03-16 South East 8
## 547 2020-03-17 South East 7
## 548 2020-03-18 South East 10
## 549 2020-03-19 South East 9
## 550 2020-03-20 South East 14
## 551 2020-03-21 South East 7
## 552 2020-03-22 South East 25
## 553 2020-03-23 South East 20
## 554 2020-03-24 South East 22
## 555 2020-03-25 South East 29
## 556 2020-03-26 South East 34
## 557 2020-03-27 South East 34
## 558 2020-03-28 South East 36
## 559 2020-03-29 South East 54
## 560 2020-03-30 South East 58
## 561 2020-03-31 South East 65
## 562 2020-04-01 South East 66
## 563 2020-04-02 South East 55
## 564 2020-04-03 South East 72
## 565 2020-04-04 South East 80
## 566 2020-04-05 South East 82
## 567 2020-04-06 South East 88
## 568 2020-04-07 South East 100
## 569 2020-04-08 South East 83
## 570 2020-04-09 South East 104
## 571 2020-04-10 South East 88
## 572 2020-04-11 South East 88
## 573 2020-04-12 South East 88
## 574 2020-04-13 South East 84
## 575 2020-04-14 South East 65
## 576 2020-04-15 South East 72
## 577 2020-04-16 South East 56
## 578 2020-04-17 South East 86
## 579 2020-04-18 South East 57
## 580 2020-04-19 South East 70
## 581 2020-04-20 South East 86
## 582 2020-04-21 South East 50
## 583 2020-04-22 South East 54
## 584 2020-04-23 South East 57
## 585 2020-04-24 South East 64
## 586 2020-04-25 South East 51
## 587 2020-04-26 South East 51
## 588 2020-04-27 South East 40
## 589 2020-04-28 South East 40
## 590 2020-04-29 South East 47
## 591 2020-04-30 South East 29
## 592 2020-05-01 South East 37
## 593 2020-05-02 South East 36
## 594 2020-05-03 South East 17
## 595 2020-05-04 South East 35
## 596 2020-05-05 South East 29
## 597 2020-05-06 South East 25
## 598 2020-05-07 South East 27
## 599 2020-05-08 South East 26
## 600 2020-05-09 South East 28
## 601 2020-05-10 South East 19
## 602 2020-05-11 South East 25
## 603 2020-05-12 South East 27
## 604 2020-05-13 South East 18
## 605 2020-05-14 South East 32
## 606 2020-05-15 South East 24
## 607 2020-05-16 South East 22
## 608 2020-05-17 South East 18
## 609 2020-05-18 South East 22
## 610 2020-05-19 South East 12
## 611 2020-05-20 South East 22
## 612 2020-05-21 South East 14
## 613 2020-05-22 South East 17
## 614 2020-05-23 South East 21
## 615 2020-05-24 South East 16
## 616 2020-05-25 South East 13
## 617 2020-05-26 South East 19
## 618 2020-05-27 South East 17
## 619 2020-05-28 South East 12
## 620 2020-05-29 South East 18
## 621 2020-05-30 South East 8
## 622 2020-05-31 South East 10
## 623 2020-06-01 South East 11
## 624 2020-06-02 South East 12
## 625 2020-06-03 South East 17
## 626 2020-06-04 South East 11
## 627 2020-06-05 South East 9
## 628 2020-06-06 South East 9
## 629 2020-06-07 South East 11
## 630 2020-06-08 South East 5
## 631 2020-06-09 South East 9
## 632 2020-06-10 South East 8
## 633 2020-06-11 South East 3
## 634 2020-06-12 South East 5
## 635 2020-06-13 South East 1
## 636 2020-06-14 South East 1
## 637 2020-03-01 South West 0
## 638 2020-03-02 South West 0
## 639 2020-03-03 South West 0
## 640 2020-03-04 South West 0
## 641 2020-03-05 South West 0
## 642 2020-03-06 South West 0
## 643 2020-03-07 South West 0
## 644 2020-03-08 South West 0
## 645 2020-03-09 South West 0
## 646 2020-03-10 South West 0
## 647 2020-03-11 South West 1
## 648 2020-03-12 South West 0
## 649 2020-03-13 South West 0
## 650 2020-03-14 South West 1
## 651 2020-03-15 South West 0
## 652 2020-03-16 South West 0
## 653 2020-03-17 South West 2
## 654 2020-03-18 South West 2
## 655 2020-03-19 South West 5
## 656 2020-03-20 South West 3
## 657 2020-03-21 South West 6
## 658 2020-03-22 South West 9
## 659 2020-03-23 South West 9
## 660 2020-03-24 South West 7
## 661 2020-03-25 South West 9
## 662 2020-03-26 South West 11
## 663 2020-03-27 South West 13
## 664 2020-03-28 South West 21
## 665 2020-03-29 South West 18
## 666 2020-03-30 South West 23
## 667 2020-03-31 South West 23
## 668 2020-04-01 South West 22
## 669 2020-04-02 South West 23
## 670 2020-04-03 South West 30
## 671 2020-04-04 South West 42
## 672 2020-04-05 South West 32
## 673 2020-04-06 South West 34
## 674 2020-04-07 South West 39
## 675 2020-04-08 South West 47
## 676 2020-04-09 South West 24
## 677 2020-04-10 South West 46
## 678 2020-04-11 South West 43
## 679 2020-04-12 South West 23
## 680 2020-04-13 South West 27
## 681 2020-04-14 South West 24
## 682 2020-04-15 South West 32
## 683 2020-04-16 South West 29
## 684 2020-04-17 South West 33
## 685 2020-04-18 South West 25
## 686 2020-04-19 South West 31
## 687 2020-04-20 South West 26
## 688 2020-04-21 South West 26
## 689 2020-04-22 South West 23
## 690 2020-04-23 South West 17
## 691 2020-04-24 South West 19
## 692 2020-04-25 South West 15
## 693 2020-04-26 South West 27
## 694 2020-04-27 South West 13
## 695 2020-04-28 South West 17
## 696 2020-04-29 South West 15
## 697 2020-04-30 South West 26
## 698 2020-05-01 South West 6
## 699 2020-05-02 South West 7
## 700 2020-05-03 South West 10
## 701 2020-05-04 South West 17
## 702 2020-05-05 South West 14
## 703 2020-05-06 South West 19
## 704 2020-05-07 South West 16
## 705 2020-05-08 South West 6
## 706 2020-05-09 South West 11
## 707 2020-05-10 South West 5
## 708 2020-05-11 South West 8
## 709 2020-05-12 South West 7
## 710 2020-05-13 South West 7
## 711 2020-05-14 South West 6
## 712 2020-05-15 South West 4
## 713 2020-05-16 South West 4
## 714 2020-05-17 South West 6
## 715 2020-05-18 South West 4
## 716 2020-05-19 South West 6
## 717 2020-05-20 South West 1
## 718 2020-05-21 South West 9
## 719 2020-05-22 South West 6
## 720 2020-05-23 South West 6
## 721 2020-05-24 South West 3
## 722 2020-05-25 South West 8
## 723 2020-05-26 South West 11
## 724 2020-05-27 South West 5
## 725 2020-05-28 South West 9
## 726 2020-05-29 South West 6
## 727 2020-05-30 South West 3
## 728 2020-05-31 South West 2
## 729 2020-06-01 South West 6
## 730 2020-06-02 South West 2
## 731 2020-06-03 South West 5
## 732 2020-06-04 South West 2
## 733 2020-06-05 South West 2
## 734 2020-06-06 South West 1
## 735 2020-06-07 South West 3
## 736 2020-06-08 South West 3
## 737 2020-06-09 South West 0
## 738 2020-06-10 South West 0
## 739 2020-06-11 South West 2
## 740 2020-06-12 South West 2
## 741 2020-06-13 South West 1
## 742 2020-06-14 South West 01.5 Completion date
We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Monday 15 Jun 2020.
1.6 Auxiliary functions
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
2 Temporal trends in NHS Pathways reports
2.1 Outline
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
2.2 Reports by region over time
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_report
2.3 Reports by region by age over time
We plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
2.4 Moving window model
We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))
2.5 Moving window model - stratified by age
We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") 
3 Comparison with deaths time series
3.1 Outline
We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
3.2 Lagged correlation
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lag
This analysis suggests that the best lag is 23 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 23 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -8.8946 -2.2215 -0.3088 2.5363 4.4960
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.000e+00 5.317e-02 94.02 <2e-16 ***
## note_lag 1.120e-05 5.240e-07 21.38 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 10.08451)
##
## Null deviance: 4980.7 on 44 degrees of freedom
## Residual deviance: 448.8 on 43 degrees of freedom
## (23 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 148.351112 1.000011
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 133.51866 164.466487
## note_lag 1.00001 1.000012
Rsq(lag_mod)
## [1] 0.9098908
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fit
4 Supplementary figures
4.1 Serial interval distribution
This is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") 
4.2 Sensitivity analysis - 7 or 21 days moving window
We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom") 
4.3 Correlation between NHS Pathways reports and deaths by NHS region
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_reg
5 Export data
We save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))6 System information
6.1 Outline
The following information documents the system on which the document was compiled.
6.2 System
This provides information on the operating system.
6.3 R environment
This provides information on the version of R used:
6.4 R packages
This provides information on the packages used:
sessionInfo()
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.1 ggpubr_0.3.0 lubridate_1.7.9
## [4] chngpt_2020.5-21 cyphr_1.1.0 DT_0.13
## [7] kableExtra_1.1.0 janitor_2.0.1 remotes_2.1.1
## [10] projections_0.4.1 earlyR_0.0.1 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.1 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.5 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.0 stringr_1.4.0
## [22] dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
## [25] tidyr_1.1.0 tibble_3.0.1 ggplot2_3.3.1
## [28] tidyverse_1.3.0 here_0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] colorspace_1.4-1 selectr_0.4-2 ggsignif_0.6.0 ellipsis_0.3.1
## [5] rprojroot_1.3-2 snakecase_0.11.0 fs_1.4.1 rstudioapi_0.11
## [9] farver_2.0.3 fansi_0.4.1 splines_3.6.3 knitr_1.28
## [13] jsonlite_1.6.1 broom_0.5.6 dbplyr_1.4.4 compiler_3.6.3
## [17] httr_1.4.1 backports_1.1.7 assertthat_0.2.1 Matrix_1.2-18
## [21] cli_2.0.2 htmltools_0.4.0 prettyunits_1.1.1 tools_3.6.3
## [25] gtable_0.3.0 glue_1.4.1 Rcpp_1.0.4.6 carData_3.0-4
## [29] cellranger_1.1.0 vctrs_0.3.1 nlme_3.1-144 matchmaker_0.1.1
## [33] crosstalk_1.1.0.1 xfun_0.14 ps_1.3.3 openxlsx_4.1.5
## [37] lifecycle_0.2.0 rstatix_0.5.0 MASS_7.3-51.5 scales_1.1.1
## [41] hms_0.5.3 sodium_1.1 yaml_2.2.1 curl_4.3
## [45] gridExtra_2.3 stringi_1.4.6 kyotil_2019.11-22 boot_1.3-24
## [49] pkgbuild_1.0.8 zip_2.0.4 rlang_0.4.6 pkgconfig_2.0.3
## [53] evaluate_0.14 lattice_0.20-38 labeling_0.3 htmlwidgets_1.5.1
## [57] cowplot_1.0.0 processx_3.4.2 tidyselect_1.1.0 plyr_1.8.6
## [61] magrittr_1.5 R6_2.4.1 generics_0.0.2 DBI_1.1.0
## [65] pillar_1.4.4 haven_2.3.1 foreign_0.8-75 withr_2.2.0
## [69] mgcv_1.8-31 survival_3.1-8 abind_1.4-5 modelr_0.1.8
## [73] crayon_1.3.4 car_3.0-8 utf8_1.1.4 rmarkdown_2.2
## [77] viridis_0.5.1 grid_3.6.3 readxl_1.3.1 data.table_1.12.8
## [81] blob_1.2.1 callr_3.4.3 reprex_0.3.0 digest_0.6.25
## [85] webshot_0.5.2 munsell_0.5.0 viridisLite_0.3.0